results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000005.fa

results/orthofinder/orthosnap/OG0000000/OG0000000_orthosnap_0.fa

results/alignment/trimmed_cds/OG0000002.trimmed.cds.alignment.fa
results/alignment/trimmed_cds/OG0000005.trimmed.cds.alignment.fa

General Characteristics
=======================
5	Number of taxa
2385	Alignment length
306	Parsimony informative sites
306	Variable sites
2055	Constant sites

Character Frequencies
=====================
T	3746
G	2296
C	1878
A	3864
-	141

IQ-TREE 2.2.0.3 COVID-edition built Sep  5 2022

Input file name: results/supermatrix/supermatrix.cds.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 847886

REFERENCES
----------

To cite IQ-TREE please use:

Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015

To cite ModelFinder please use: 

Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285

Since you used ultrafast bootstrap (UFBoot) please also cite: 

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281

SEQUENCE ALIGNMENT
------------------

Input data: 5 sequences with 2385 nucleotide sites
Number of constant sites: 1470 (= 61.6352% of all sites)
Number of invariant (constant or ambiguous constant) sites: 1470 (= 61.6352% of all sites)
Number of parsimony informative sites: 306
Number of distinct site patterns: 333

ModelFinder
-----------

Best-fit model according to BIC: TIM2+F+G4

List of models sorted by BIC scores: 

Model                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC
TIM2+F+G4         -8644.524   17317.049 -  0.00536   17317.226 -  0.00553   17397.926 +    0.568
TIM2+F+I          -8645.577   17319.154 -  0.00187   17319.332 -  0.00193   17400.032 +    0.198
GTR+F+G4          -8637.895   17307.790 +    0.549   17308.020 +    0.552   17400.222 +     0.18
TIM2+F+I+G4       -8643.755   17317.510 -  0.00426   17317.713 -  0.00434   17404.165 -   0.0251
GTR+F+I           -8640.051   17312.102 +   0.0636   17312.332 +   0.0639   17404.534 -   0.0209
GTR+F+I+G4        -8637.276   17308.552 +    0.375   17308.810 +    0.372   17406.760 -  0.00686
TPM2u+F+I         -8662.959   17351.917 - 1.44e-10   17352.071 -  1.5e-10   17427.018 - 2.74e-07
TPM2u+F+G4        -8663.083   17352.165 - 1.27e-10   17352.319 - 1.33e-10   17427.266 - 2.42e-07
TVM+F+G4          -8656.156   17342.312 - 1.75e-08   17342.515 - 1.79e-08   17428.966 - 1.03e-07
TVM+F+I           -8657.334   17344.669 - 5.39e-09   17344.871 -  5.5e-09   17431.323 - 3.18e-08
TPM2u+F+I+G4      -8662.520   17353.040 -  8.2e-11   17353.218 - 8.46e-11   17433.918 - 8.69e-09
TVM+F+I+G4        -8655.724   17343.448 - 9.93e-09   17343.678 - 9.98e-09   17435.879 - 3.26e-09
TIM+F+G4          -8664.187   17356.373 - 1.55e-11   17356.551 -  1.6e-11   17437.251 - 1.64e-09
TIM+F+I           -8665.228   17358.456 - 5.47e-12   17358.633 - 5.65e-12   17439.333 -  5.8e-10
TIM+F+I+G4        -8663.563   17357.127 - 1.06e-11   17357.329 - 1.08e-11   17443.781 - 6.27e-11
K3Pu+F+I          -8682.883   17391.765 -  3.2e-19   17391.919 - 3.34e-19   17466.866 - 6.09e-16
K3Pu+F+G4         -8684.608   17395.216 -  5.7e-20   17395.370 - 5.95e-20   17470.316 - 1.08e-16
TN+F+G4           -8687.700   17401.400 - 2.59e-21   17401.554 -  2.7e-21   17476.501 - 4.93e-18
TN+F+I            -8687.772   17401.544 - 2.41e-21   17401.698 - 2.51e-21   17476.645 - 4.58e-18
K3Pu+F+I+G4       -8684.009   17396.017 - 3.82e-20   17396.194 - 3.94e-20   17476.894 - 4.05e-18
TN+F+I+G4         -8686.938   17401.875 - 2.04e-21   17402.052 - 2.11e-21   17482.753 - 2.16e-19
TIM3+F+G4         -8687.180   17402.360 -  1.6e-21   17402.537 - 1.65e-21   17483.237 -  1.7e-19
TIM3+F+I          -8687.222   17402.443 - 1.54e-21   17402.621 - 1.58e-21   17483.321 - 1.63e-19
TIM3+F+I+G4       -8686.492   17402.983 - 1.17e-21   17403.186 - 1.19e-21   17489.637 - 6.92e-21
HKY+F+I           -8703.893   17431.786 - 6.52e-28   17431.918 - 6.89e-28   17501.110 - 2.23e-23
HKY+F+G4          -8706.824   17437.648 - 3.48e-29   17437.780 - 3.67e-29   17506.972 - 1.19e-24
TPM3u+F+I         -8703.263   17432.526 - 4.51e-28   17432.680 - 4.71e-28   17507.627 - 8.58e-25
HKY+F+I+G4        -8706.070   17438.140 - 2.72e-29   17438.294 - 2.84e-29   17513.241 - 5.18e-26
TPM3u+F+G4        -8706.096   17438.192 - 2.65e-29   17438.346 - 2.77e-29   17513.293 - 5.05e-26
TPM3u+F+I+G4      -8705.389   17438.778 - 1.98e-29   17438.955 - 2.04e-29   17519.655 -  2.1e-27
F81+F+I           -8719.214   17460.427 - 3.94e-34   17460.539 -  4.2e-34   17523.974 - 2.42e-28
F81+F+G4          -8722.344   17466.689 - 1.72e-35   17466.800 - 1.83e-35   17530.235 - 1.06e-29
F81+F+I+G4        -8721.576   17467.153 - 1.36e-35   17467.284 - 1.44e-35   17536.476 - 4.67e-31
SYM+G4            -8781.326   17588.651 - 5.65e-62   17588.805 -  5.9e-62   17663.752 - 1.07e-58
SYM+I             -8781.958   17589.916 -    3e-62   17590.069 - 3.13e-62   17665.016 - 5.71e-59
TVMe+G4           -8788.184   17600.368 - 1.61e-64   17600.500 -  1.7e-64   17669.692 - 5.51e-60
SYM+I+G4          -8780.617   17589.235 - 4.22e-62   17589.412 - 4.35e-62   17670.112 - 4.47e-60
TVMe+I            -8788.416   17600.832 - 1.28e-64   17600.964 - 1.35e-64   17670.156 - 4.37e-60
TVMe+I+G4         -8787.408   17600.816 - 1.29e-64   17600.970 - 1.35e-64   17675.916 - 2.45e-61
GTR+F             -8790.985   17611.970 - 4.88e-67   17612.172 - 4.97e-67   17698.624 - 2.88e-66
TIM2e+I           -8821.675   17665.350 - 1.25e-78   17665.461 - 1.33e-78   17728.896 - 7.68e-73
TIM2e+G4          -8822.942   17667.884 - 3.52e-79   17667.995 - 3.75e-79   17731.431 - 2.16e-73
TPM2+I            -8827.241   17674.482 -  1.3e-80   17674.574 -  1.4e-80   17732.251 - 1.44e-73
TPM2+G4           -8828.311   17676.623 - 4.46e-81   17676.716 -  4.8e-81   17734.392 - 4.92e-74
TIM2e+I+G4        -8821.803   17667.606 - 4.04e-79   17667.738 - 4.27e-79   17736.930 - 1.38e-74
TPM2+I+G4         -8827.262   17676.523 - 4.68e-81   17676.634 -    5e-81   17740.070 - 2.88e-75
TIMe+I            -8855.392   17732.785 - 2.84e-93   17732.896 - 3.03e-93   17796.331 - 1.75e-87
TIMe+G4           -8858.213   17738.427 - 1.69e-94   17738.538 -  1.8e-94   17801.973 - 1.04e-88
K3P+I             -8862.515   17745.031 - 6.23e-96   17745.124 -  6.7e-96   17802.801 - 6.88e-89
TIMe+I+G4         -8857.149   17738.298 -  1.8e-94   17738.429 - 1.91e-94   17807.621 - 6.17e-90
K3P+G4            -8866.996   17753.992 - 7.05e-98   17754.085 - 7.59e-98   17811.762 - 7.79e-91
K3P+I+G4          -8865.784   17753.567 - 8.72e-98   17753.679 -  9.3e-98   17817.114 - 5.36e-92
TIM3e+I           -8888.262   17798.523 - 1.51e-107   17798.635 - 1.61e-107   17862.070 - 9.27e-102
TIM3e+G4          -8888.442   17798.883 - 1.26e-107   17798.994 - 1.34e-107   17862.430 - 7.74e-102
TIM3e+I+G4        -8887.719   17799.439 - 9.55e-108   17799.570 - 1.01e-107   17868.762 - 3.27e-103
TPM3+I            -8896.995   17813.990 - 6.61e-111   17814.083 - 7.11e-111   17871.759 - 7.3e-104
TPM3+G4           -8899.610   17819.219 - 4.84e-112   17819.312 - 5.21e-112   17876.989 - 5.34e-105
TPM3+I+G4         -8898.394   17818.788 -   6e-112   17818.900 - 6.4e-112   17882.335 - 3.69e-106
TNe+I             -8913.640   17847.281 - 3.9e-118   17847.374 - 4.2e-118   17905.050 - 4.31e-111
JC+I              -8924.092   17864.183 - 8.33e-122   17864.244 - 9.11e-122   17910.399 - 2.97e-112
TNe+G4            -8916.622   17853.244 - 1.98e-119   17853.337 - 2.13e-119   17911.014 - 2.18e-112
K2P+I             -8921.214   17860.427 - 5.45e-121   17860.503 - 5.92e-121   17912.420 - 1.08e-112
TNe+I+G4          -8915.437   17852.875 - 2.38e-119   17852.986 - 2.54e-119   17916.421 - 1.46e-113
JC+G4             -8929.121   17874.242 - 5.45e-124   17874.302 - 5.96e-124   17920.457 - 1.94e-114
K2P+G4            -8925.819   17869.638 - 5.45e-123   17869.714 - 5.91e-123   17921.631 - 1.08e-114
JC+I+G4           -8927.689   17873.377 - 8.4e-124   17873.453 - 9.12e-124   17925.370 - 1.67e-115
K2P+I+G4          -8924.449   17868.897 - 7.89e-123   17868.990 - 8.49e-123   17926.667 - 8.71e-116

AIC, w-AIC   : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC   : Bayesian information criterion scores and weights.

Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.

SUBSTITUTION PROCESS
--------------------

Model of substitution: TIM2+F+G4

Rate parameter R:

  A-C: 2.8978
  A-G: 2.1991
  A-T: 2.8978
  C-G: 1.0000
  C-T: 4.8497
  G-T: 1.0000

State frequencies: (empirical counts from alignment)

  pi(A) = 0.3279
  pi(C) = 0.1594
  pi(G) = 0.1948
  pi(T) = 0.3179

Rate matrix Q:

  A   -0.9711    0.2476    0.2297    0.4938
  C    0.5094     -1.44    0.1045    0.8265
  G    0.3866   0.08544   -0.6424    0.1704
  T    0.5094    0.4143    0.1045    -1.028

Model of rate heterogeneity: Gamma with 4 categories
Gamma shape alpha: 0.4419

 Category  Relative_rate  Proportion
  1         0.02319        0.25
  2         0.2123         0.25
  3         0.7729         0.25
  4         2.992          0.25
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.

MAXIMUM LIKELIHOOD TREE
-----------------------

Log-likelihood of the tree: -8644.5237 (s.e. 119.3831)
Unconstrained log-likelihood (without tree): -8461.5941
Number of free parameters (#branches + #model parameters): 14
Akaike information criterion (AIC) score: 17317.0475
Corrected Akaike information criterion (AICc) score: 17317.2247
Bayesian information criterion (BIC) score: 17397.9248

Total tree length (sum of branch lengths): 0.8645
Sum of internal branch lengths: 0.1208 (13.9718% of tree length)

NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are  ultrafast bootstrap support (%)

+--------------------------------------------------Avrainvillea_mazei_HV02664
|
+------------------------------------------------Bryopsis_plumosa_WEST4718
|
|                 +----------------------------------------Caulerpa_cliftonii_HV03798
+-----------------| (100)
                  |          +-----------------------Chlorodesmis_fastigiata_HV03865
                  +----------| (99)
                             +------------------Flabellia_petiolata_HV01202

Tree in newick format:

(Avrainvillea_mazei_HV02664:0.2056761456,Bryopsis_plumosa_WEST4718:0.1993163343,(Caulerpa_cliftonii_HV03798:0.1651557220,(Chlorodesmis_fastigiata_HV03865:0.0968954934,Flabellia_petiolata_HV01202:0.0766739628)99:0.0449103250)100:0.0758766912);

CONSENSUS TREE
--------------

Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -8644.523737
Robinson-Foulds distance between ML tree and consensus tree: 0

Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)

+--------------------------------------------------Avrainvillea_mazei_HV02664
|
+------------------------------------------------Bryopsis_plumosa_WEST4718
|
|                 +----------------------------------------Caulerpa_cliftonii_HV03798
+-----------------| (100)
                  |          +-----------------------Chlorodesmis_fastigiata_HV03865
                  +----------| (99)
                             +------------------Flabellia_petiolata_HV01202


Consensus tree in newick format: 

(Avrainvillea_mazei_HV02664:0.2056779366,Bryopsis_plumosa_WEST4718:0.1993172073,(Caulerpa_cliftonii_HV03798:0.1651639861,(Chlorodesmis_fastigiata_HV03865:0.0968984229,Flabellia_petiolata_HV01202:0.0766746703)99:0.0448985140)100:0.0758763360);

TIME STAMP
----------

Date and time: Fri Jan 19 23:44:30 2024
Total CPU time used: 8.138436 seconds (0h:0m:8s)
Total wall-clock time used: 0.786878109 seconds (0h:0m:0s)

IQ-TREE multicore version 2.2.0.3 COVID-edition for Mac OS X 64-bit built Sep  5 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.

Host:    9350L-152779-M (AVX2, FMA3, 16 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.cds.fa -bb 1000 -m TEST -nt 12 -redo
Seed:    847886 (Using SPRNG - Scalable Parallel Random Number Generator)
Time:    Fri Jan 19 23:44:28 2024
Kernel:  AVX+FMA - 12 threads (12 CPU cores detected)

Reading alignment file results/supermatrix/supermatrix.cds.fa ... Fasta format detected
Reading fasta file: done in 0.000298977 secs using 83.62% CPU
Alignment most likely contains DNA/RNA sequences
Constructing alignment: done in 0.00398088 secs using 1024% CPU
Alignment has 5 sequences with 2385 columns, 333 distinct patterns
306 parsimony-informative, 609 singleton sites, 1470 constant sites
                                 Gap/Ambiguity  Composition  p-value
Analyzing sequences: done in 1.78814e-05 secs using 67.11% CPU
   1  Avrainvillea_mazei_HV02664         1.01%    passed     32.15%
   2  Bryopsis_plumosa_WEST4718          0.38%    passed     79.51%
   3  Caulerpa_cliftonii_HV03798         0.88%    passed     49.91%
   4  Chlorodesmis_fastigiata_HV03865    1.38%    passed     92.09%
   5  Flabellia_petiolata_HV01202        2.26%    passed     94.37%
****  TOTAL                              1.18%  0 sequences failed composition chi2 test (p-value<5%; df=3)
Checking for duplicate sequences: done in 7.29561e-05 secs using 749.8% CPU


Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.002 seconds
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Perform fast likelihood tree search using GTR+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.000669003 secs using 275.2% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -8637.254
Optimal log-likelihood: -8637.251
Rate parameters:  A-C: 2.96313  A-G: 2.93933  A-T: 4.21828  C-G: 2.01322  C-T: 6.41081  G-T: 1.00000
Base frequencies:  A: 0.328  C: 0.159  G: 0.195  T: 0.318
Proportion of invariable sites: 0.308
Gamma shape alpha: 1.115
Parameters optimization took 1 rounds (0.001 sec)
Time for fast ML tree search: 0.011 seconds

NOTE: ModelFinder requires 1 MB RAM!
ModelFinder will test up to 88 DNA models (sample size: 2385) ...
 No. Model         -LnL         df  AIC          AICc         BIC
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  1  GTR+F         8790.985     15  17611.970    17612.172    17698.624
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  2  GTR+F+I       8640.051     16  17312.102    17312.332    17404.534
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  3  GTR+F+G4      8637.895     16  17307.790    17308.020    17400.222
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  4  GTR+F+I+G4    8637.276     17  17308.552    17308.810    17406.760
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  6  SYM+I         8781.958     13  17589.916    17590.069    17665.016
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  7  SYM+G4        8781.326     13  17588.651    17588.805    17663.752
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  8  SYM+I+G4      8780.617     14  17589.235    17589.412    17670.112
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 10  TVM+F+I       8657.334     15  17344.669    17344.871    17431.323
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 11  TVM+F+G4      8656.156     15  17342.312    17342.515    17428.966
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 12  TVM+F+I+G4    8655.724     16  17343.448    17343.678    17435.879
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 14  TVMe+I        8788.416     12  17600.832    17600.964    17670.156
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 15  TVMe+G4       8788.184     12  17600.368    17600.500    17669.692
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 16  TVMe+I+G4     8787.408     13  17600.816    17600.970    17675.916
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 18  TIM3+F+I      8687.222     14  17402.443    17402.621    17483.321
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 19  TIM3+F+G4     8687.180     14  17402.360    17402.537    17483.237
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 20  TIM3+F+I+G4   8686.492     15  17402.983    17403.186    17489.637
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 22  TIM3e+I       8888.262     11  17798.523    17798.635    17862.070
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 23  TIM3e+G4      8888.442     11  17798.883    17798.994    17862.430
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 24  TIM3e+I+G4    8887.719     12  17799.439    17799.570    17868.762
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 26  TIM2+F+I      8645.577     14  17319.154    17319.332    17400.032
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 27  TIM2+F+G4     8644.524     14  17317.049    17317.226    17397.926
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 28  TIM2+F+I+G4   8643.755     15  17317.510    17317.713    17404.165
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 30  TIM2e+I       8821.675     11  17665.350    17665.461    17728.896
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 31  TIM2e+G4      8822.942     11  17667.884    17667.995    17731.431
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 32  TIM2e+I+G4    8821.803     12  17667.606    17667.738    17736.930
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 34  TIM+F+I       8665.228     14  17358.456    17358.633    17439.333
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 35  TIM+F+G4      8664.187     14  17356.373    17356.551    17437.251
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 36  TIM+F+I+G4    8663.563     15  17357.127    17357.329    17443.781
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 38  TIMe+I        8855.392     11  17732.785    17732.896    17796.331
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 39  TIMe+G4       8858.213     11  17738.427    17738.538    17801.973
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 40  TIMe+I+G4     8857.149     12  17738.298    17738.429    17807.621
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 42  TPM3u+F+I     8703.263     13  17432.526    17432.680    17507.627
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 43  TPM3u+F+G4    8706.096     13  17438.192    17438.346    17513.293
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 44  TPM3u+F+I+G4  8705.389     14  17438.778    17438.955    17519.655
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 46  TPM3+I        8896.995     10  17813.990    17814.083    17871.759
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 47  TPM3+G4       8899.610     10  17819.219    17819.312    17876.989
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 48  TPM3+I+G4     8898.394     11  17818.788    17818.900    17882.335
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 50  TPM2u+F+I     8662.959     13  17351.917    17352.071    17427.018
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 51  TPM2u+F+G4    8663.083     13  17352.165    17352.319    17427.266
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 52  TPM2u+F+I+G4  8662.520     14  17353.040    17353.218    17433.918
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 54  TPM2+I        8827.241     10  17674.482    17674.574    17732.251
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 55  TPM2+G4       8828.311     10  17676.623    17676.716    17734.392
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 56  TPM2+I+G4     8827.262     11  17676.523    17676.634    17740.070
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 58  K3Pu+F+I      8682.883     13  17391.765    17391.919    17466.866
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 59  K3Pu+F+G4     8684.608     13  17395.216    17395.370    17470.316
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 60  K3Pu+F+I+G4   8684.009     14  17396.017    17396.194    17476.894
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 62  K3P+I         8862.515     10  17745.031    17745.124    17802.801
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 63  K3P+G4        8866.996     10  17753.992    17754.085    17811.762
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 64  K3P+I+G4      8865.784     11  17753.567    17753.679    17817.114
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 66  TN+F+I        8687.772     13  17401.544    17401.698    17476.645
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 67  TN+F+G4       8687.700     13  17401.400    17401.554    17476.501
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 68  TN+F+I+G4     8686.938     14  17401.875    17402.052    17482.753
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 70  TNe+I         8913.640     10  17847.281    17847.374    17905.050
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 71  TNe+G4        8916.622     10  17853.244    17853.337    17911.014
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 72  TNe+I+G4      8915.437     11  17852.875    17852.986    17916.421
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 74  HKY+F+I       8703.893     12  17431.786    17431.918    17501.110
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 75  HKY+F+G4      8706.824     12  17437.648    17437.780    17506.972
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 76  HKY+F+I+G4    8706.070     13  17438.140    17438.294    17513.241
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 78  K2P+I         8921.214     9   17860.427    17860.503    17912.420
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 79  K2P+G4        8925.819     9   17869.638    17869.714    17921.631
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 80  K2P+I+G4      8924.449     10  17868.897    17868.990    17926.667
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 82  F81+F+I       8719.214     11  17460.427    17460.539    17523.974
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 83  F81+F+G4      8722.344     11  17466.689    17466.800    17530.235
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 84  F81+F+I+G4    8721.576     12  17467.153    17467.284    17536.476
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 86  JC+I          8924.092     8   17864.183    17864.244    17910.399
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 87  JC+G4         8929.121     8   17874.242    17874.302    17920.457
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 88  JC+I+G4       8927.689     9   17873.377    17873.453    17925.370
Akaike Information Criterion:           GTR+F+G4
Corrected Akaike Information Criterion: GTR+F+G4
Bayesian Information Criterion:         TIM2+F+G4
Best-fit model: TIM2+F+G4 chosen according to BIC

All model information printed to results/supermatrix/supermatrix.cds.fa.model.gz
CPU time for ModelFinder: 3.625 seconds (0h:0m:3s)
Wall-clock time for ModelFinder: 0.349 seconds (0h:0m:0s)
Generating 1000 samples for ultrafast bootstrap (seed: 847886)...

NOTE: 1 MB RAM (0 GB) is required!
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Estimate model parameters (epsilon = 0.100)
1. Initial log-likelihood: -8644.524
Optimal log-likelihood: -8644.524
Rate parameters:  A-C: 2.89399  A-G: 2.20137  A-T: 2.89399  C-G: 1.00000  C-T: 4.84920  G-T: 1.00000
Base frequencies:  A: 0.328  C: 0.159  G: 0.195  T: 0.318
Gamma shape alpha: 0.442
Parameters optimization took 1 rounds (0.001 sec)
Wrote distance file to... 
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.000123024 secs using 1399% CPU
Computing ML distances took 0.000236 sec (of wall-clock time) 0.002547 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 7.82013e-05 secs using 676.5% CPU
Constructing RapidNJ tree: done in 0.000242949 secs using 299.2% CPU
Computing RapidNJ tree took 0.000470 sec (of wall-clock time) 0.002205 sec (of CPU time)
Log-likelihood of RapidNJ tree: -8644.524
--------------------------------------------------------------------
|             INITIALIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.031 second
Computing log-likelihood of 4 initial trees ... 0.001 seconds
Current best score: -8644.524

Do NNI search on 5 best initial trees
Optimizing NNI: done in 0.000752926 secs using 226.3% CPU
Optimizing NNI: done in 0.00160408 secs using 741.1% CPU
Optimizing NNI: done in 0.00219321 secs using 1157% CPU
Optimizing NNI: done in 0.00180602 secs using 872.4% CPU
Optimizing NNI: done in 0.00163507 secs using 334.6% CPU
Finish initializing candidate tree set (5)
Current best tree score: -8644.524 / CPU time: 0.045
Number of iterations: 5
--------------------------------------------------------------------
|               OPTIMIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Optimizing NNI: done in 0.00235391 secs using 259.2% CPU
Optimizing NNI: done in 0.00234509 secs using 1062% CPU
Optimizing NNI: done in 0.00151801 secs using 99.74% CPU
Optimizing NNI: done in 0.00404 secs using 457.9% CPU
Optimizing NNI: done in 0.00143313 secs using 99.64% CPU
Iteration 10 / LogL: -8644.539 / Time: 0h:0m:0s
Optimizing NNI: done in 0.000659943 secs using 99.25% CPU
Optimizing NNI: done in 0.00792503 secs using 743.3% CPU
Optimizing NNI: done in 0.00179005 secs using 423.4% CPU
Optimizing NNI: done in 0.00468278 secs using 958.8% CPU
Optimizing NNI: done in 0.00144506 secs using 99.58% CPU
Optimizing NNI: done in 0.00532413 secs using 1041% CPU
Optimizing NNI: done in 0.00345707 secs using 922.1% CPU
Optimizing NNI: done in 0.00262403 secs using 645.3% CPU
Optimizing NNI: done in 0.000674009 secs using 99.7% CPU
Optimizing NNI: done in 0.00239396 secs using 449.7% CPU
Iteration 20 / LogL: -8644.529 / Time: 0h:0m:0s
Optimizing NNI: done in 0.000617027 secs using 99.67% CPU
Optimizing NNI: done in 0.000699997 secs using 246.7% CPU
Optimizing NNI: done in 0.000654936 secs using 99.55% CPU
Optimizing NNI: done in 0.00351095 secs using 876.3% CPU
Optimizing NNI: done in 0.00156808 secs using 99.61% CPU
Optimizing NNI: done in 0.000607014 secs using 99.5% CPU
Optimizing NNI: done in 0.00241089 secs using 539.2% CPU
Optimizing NNI: done in 0.0024519 secs using 99.72% CPU
Optimizing NNI: done in 0.00138903 secs using 213.5% CPU
UPDATE BEST LOG-LIKELIHOOD: -8644.524
Optimizing NNI: done in 0.00257397 secs using 860.3% CPU
Iteration 30 / LogL: -8644.813 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00520802 secs using 877.5% CPU
Optimizing NNI: done in 0.00559211 secs using 685.3% CPU
Optimizing NNI: done in 0.00432086 secs using 973.5% CPU
Optimizing NNI: done in 0.001266 secs using 1121% CPU
UPDATE BEST LOG-LIKELIHOOD: -8644.524
Optimizing NNI: done in 0.00166202 secs using 829.7% CPU
Optimizing NNI: done in 0.000695944 secs using 539.6% CPU
Optimizing NNI: done in 0.00202012 secs using 839.6% CPU
Optimizing NNI: done in 0.00322795 secs using 996.2% CPU
Optimizing NNI: done in 0.00426793 secs using 859.4% CPU
Optimizing NNI: done in 0.00254703 secs using 1052% CPU
Iteration 40 / LogL: -8644.813 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00221777 secs using 99.29% CPU
Optimizing NNI: done in 0.0010829 secs using 420.4% CPU
Optimizing NNI: done in 0.0026629 secs using 442.6% CPU
Optimizing NNI: done in 0.00244284 secs using 524.3% CPU
Optimizing NNI: done in 0.00243592 secs using 496.2% CPU
Optimizing NNI: done in 0.0201399 secs using 875.7% CPU
Optimizing NNI: done in 0.00250101 secs using 816.7% CPU
Optimizing NNI: done in 0.0196991 secs using 922.5% CPU
Optimizing NNI: done in 0.022208 secs using 828.4% CPU
Optimizing NNI: done in 0.018327 secs using 937.9% CPU
Iteration 50 / LogL: -8644.529 / Time: 0h:0m:0s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -8660.628
Optimizing NNI: done in 0.0205791 secs using 876.5% CPU
Optimizing NNI: done in 0.000946999 secs using 610.1% CPU
Optimizing NNI: done in 0.00535607 secs using 921.1% CPU
Optimizing NNI: done in 0.00240302 secs using 1069% CPU
Optimizing NNI: done in 0.000684977 secs using 854.9% CPU
Optimizing NNI: done in 0.0114281 secs using 1027% CPU
Optimizing NNI: done in 0.000825882 secs using 392.9% CPU
Optimizing NNI: done in 0.000998974 secs using 1009% CPU
Optimizing NNI: done in 0.00154591 secs using 1237% CPU
Optimizing NNI: done in 0.00153995 secs using 292% CPU
Iteration 60 / LogL: -8644.532 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00385499 secs using 1080% CPU
Optimizing NNI: done in 0.003232 secs using 658.4% CPU
Optimizing NNI: done in 0.000845909 secs using 567.8% CPU
Optimizing NNI: done in 0.0024941 secs using 1220% CPU
Optimizing NNI: done in 0.00177002 secs using 728.4% CPU
Optimizing NNI: done in 0.00148296 secs using 662.6% CPU
Optimizing NNI: done in 0.00132489 secs using 99.71% CPU
Optimizing NNI: done in 0.00409603 secs using 1075% CPU
Optimizing NNI: done in 0.00148892 secs using 1095% CPU
Optimizing NNI: done in 0.0022881 secs using 111.8% CPU
Iteration 70 / LogL: -8644.529 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00163007 secs using 1043% CPU
Optimizing NNI: done in 0.00340295 secs using 819.4% CPU
Optimizing NNI: done in 0.00157595 secs using 586% CPU
Optimizing NNI: done in 0.00146103 secs using 173.2% CPU
Optimizing NNI: done in 0.00464201 secs using 791% CPU
Optimizing NNI: done in 0.00275683 secs using 627.2% CPU
Optimizing NNI: done in 0.00241899 secs using 99.42% CPU
Optimizing NNI: done in 0.00256109 secs using 471.8% CPU
Optimizing NNI: done in 0.00149703 secs using 203.7% CPU
Optimizing NNI: done in 0.00127912 secs using 310.4% CPU
Iteration 80 / LogL: -8644.524 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.003613 secs using 604.3% CPU
Optimizing NNI: done in 0.00226617 secs using 99.77% CPU
Optimizing NNI: done in 0.003299 secs using 810.6% CPU
Optimizing NNI: done in 0.00107002 secs using 571.6% CPU
Optimizing NNI: done in 0.014076 secs using 923.8% CPU
Optimizing NNI: done in 0.00147605 secs using 1228% CPU
Optimizing NNI: done in 0.00224209 secs using 99.77% CPU
Optimizing NNI: done in 0.00308704 secs using 1115% CPU
Optimizing NNI: done in 0.00383687 secs using 1155% CPU
Optimizing NNI: done in 0.00152683 secs using 1221% CPU
Iteration 90 / LogL: -8644.529 / Time: 0h:0m:0s (0h:0m:0s left)
Optimizing NNI: done in 0.00225902 secs using 817.8% CPU
Optimizing NNI: done in 0.00230694 secs using 847.7% CPU
Optimizing NNI: done in 0.000684023 secs using 99.7% CPU
Optimizing NNI: done in 0.00258899 secs using 652.2% CPU
Optimizing NNI: done in 0.002388 secs using 99.75% CPU
Optimizing NNI: done in 0.00198388 secs using 986.1% CPU
Optimizing NNI: done in 0.00253201 secs using 787.3% CPU
Optimizing NNI: done in 0.00301313 secs using 995.3% CPU
Optimizing NNI: done in 0.000639915 secs using 99.7% CPU
Optimizing NNI: done in 0.00150895 secs using 202.1% CPU
Iteration 100 / LogL: -8644.668 / Time: 0h:0m:0s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -8660.628
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.00335908 secs using 676% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:0s

--------------------------------------------------------------------
|                    FINALIZING TREE SEARCH                        |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -8644.524
Optimal log-likelihood: -8644.524
Rate parameters:  A-C: 2.89783  A-G: 2.19908  A-T: 2.89783  C-G: 1.00000  C-T: 4.84972  G-T: 1.00000
Base frequencies:  A: 0.328  C: 0.159  G: 0.195  T: 0.318
Gamma shape alpha: 0.442
Parameters optimization took 1 rounds (0.001 sec)
BEST SCORE FOUND : -8644.524
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.cds.fa.splits.nex
Total tree length: 0.865

Total number of iterations: 101
CPU time used for tree search: 7.433 sec (0h:0m:7s)
Wall-clock time used for tree search: 0.714 sec (0h:0m:0s)
Total CPU time used: 8.138 sec (0h:0m:8s)
Total wall-clock time used: 0.779 sec (0h:0m:0s)

Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.cds.fa.splits.nex...
5 taxa and 9 splits.
Consensus tree written to results/supermatrix/supermatrix.cds.fa.contree
Reading input trees file results/supermatrix/supermatrix.cds.fa.contree
Log-likelihood of consensus tree: -8644.524

Analysis results written to: 
  IQ-TREE report:                results/supermatrix/supermatrix.cds.fa.iqtree
  Maximum-likelihood tree:       results/supermatrix/supermatrix.cds.fa.treefile
  Likelihood distances:          results/supermatrix/supermatrix.cds.fa.mldist

Ultrafast bootstrap approximation results written to:
  Split support values:          results/supermatrix/supermatrix.cds.fa.splits.nex
  Consensus tree:                results/supermatrix/supermatrix.cds.fa.contree
  Screen log file:               results/supermatrix/supermatrix.cds.fa.log

ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.cds.fa.treefile -m "TIM2{2.89783,2.19908,4.84972}+F{0.327902,0.159369,0.19484,0.317889}+G4{0.441906}" --length 2385

Date and Time: Fri Jan 19 23:44:30 2024

@article{10.1093/molbev/mst010,
    author = "Katoh, Kazutaka and Standley, Daron M.",
    title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
    journal = "Molecular Biology and Evolution",
    volume = "30",
    number = "4",
    pages = "772-780",
    year = "2013",
    month = "01",
    abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/mst010",
    url = "https://doi.org/10.1093/molbev/mst010",
    eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}

@article{Emms2019,
    author = "Emms, David M. and Kelly, Steven",
    type = "Journal Article",
    title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
    journal = "Genome Biology",
    number = "1",
    doi = "10.1186/s13059-019-1832-y",
    volume = "20",
    pages = "238",
    url = "https://doi.org/10.1186/s13059-019-1832-y",
    year = "2019",
    abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
    isbn = "1474-760X",
    DA = "2019/11/14"
}

@article{10.1371/journal.pbio.3001007,
    author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    doi = "10.1371/journal.pbio.3001007",
    journal = "PLOS Biology",
    publisher = "Public Library of Science",
    title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
    year = "2020",
    month = "12",
    volume = "18",
    url = "https://doi.org/10.1371/journal.pbio.3001007",
    pages = "1-17",
    abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
    number = "12"
}

@article{Steenwyk_2021,
    author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
    doi = "10.1101/2021.10.02.462868",
    url = "https://doi.org/10.1101\%2F2021.10.02.462868",
    year = "2021",
    month = "oct",
    journal = "biorxiv",
    publisher = "Cold Spring Harbor Laboratory",
    title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}

@article{10.1093/molbev/msx281,
    author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
    title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
    journal = "Molecular Biology and Evolution",
    volume = "35",
    number = "2",
    pages = "518-522",
    year = "2017",
    month = "10",
    abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msx281",
    url = "https://doi.org/10.1093/molbev/msx281",
    eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}

@article{10.1093/bioinformatics/btab096,
    author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
    journal = "Bioinformatics",
    volume = "37",
    number = "16",
    pages = "2325-2331",
    year = "2021",
    month = "02",
    abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
    issn = "1367-4803",
    doi = "10.1093/bioinformatics/btab096",
    url = "https://doi.org/10.1093/bioinformatics/btab096",
    eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}

@article{eaton_toytree_2020,
    author = "Eaton, Deren A. R.",
    title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
    volume = "11",
    doi = "10.1111/2041-210X.13313",
    journal = "Methods in Ecology and Evolution",
    year = "2020",
    pages = "187--191"
}

@article{10.1093/molbev/msaa015,
    author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
    title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
    journal = "Molecular Biology and Evolution",
    volume = "37",
    number = "5",
    pages = "1530-1534",
    year = "2020",
    month = "02",
    abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msaa015",
    url = "https://doi.org/10.1093/molbev/msaa015",
    eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}

@article{Kalyaanamoorthy2017,
    author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
    type = "Journal Article",
    title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
    journal = "Nature Methods",
    number = "6",
    doi = "10.1038/nmeth.4285",
    volume = "14",
    pages = "587--589",
    url = "https://doi.org/10.1038/nmeth.4285",
    year = "2017",
    abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
    issn = "1548-7105",
    DA = "2017/06/01"
}

@article{Zhang2018,
    author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
    type = "Journal Article",
    title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
    journal = "BMC Bioinformatics",
    number = "6",
    doi = "10.1186/s12859-018-2129-y",
    volume = "19",
    pages = "153",
    url = "https://doi.org/10.1186/s12859-018-2129-y",
    year = "2018",
    abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
    issn = "1471-2105",
    DA = "2018/05/08"
}